Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography
By E. Cole, Zheng Zhang, H. Marques, R. Edward Hendrick, M. Yaffe, E. Pisano. 2014
68 citations recorded by the source corpus.
OBJECTIVE The purpose of this study was to assess the impact of computer-aided detection (CAD) systems on the performance of radiologists with digital mammograms acquired during the Digital Mammographic Imaging Screening Trial (DMIST). MATERIALS AND METHODS Only those DMIST cases with proven cancer status by biopsy or 1-year follow-up that had available digital images were included in this multireader, multicase ROC study. Two commercially available CAD systems for digital mammography were used: iCAD SecondLook, version 1.4; and R2 ImageChecker Cenova, version 1.0. Fourteen radiologists interpreted, without and with CAD, a set of 300 cases (150 cancer, 150 benign or normal) on the iCAD SecondLook system, and 15 radiologists interpreted a different set of 300 cases (150 cancer, 150 benign or normal) on the R2 ImageChecker Cenova system. RESULTS The average AUC was 0.71 (95% CI, 0.66–0.76) without and 0.72 (95% CI, 0.67–0.77) with the iCAD system (p = 0.07). Similarly, the average AUC was 0.71 (95% CI, 0.66–0.76) without and 0.72 (95% CI 0.67–0.77) with the R2 system (p = 0.08). Sensitivity and specificity differences without and with CAD for both systems also were not significant. CONCLUSION Radiologists in our studies rarely changed their diagnostic decisions after the addition of CAD. The application of CAD had no statistically significant effect on radiologist AUC, sensitivity, or specificity performance with digital mammograms from DMIST.
Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- Artificial intelligence in radiology (2018, unknown): “In examining mammograms, some studies have reported that radiologists rarely altered their diagnostic decisions after viewing results from predefined, feature-based CADe systems and that their clinical integration had no statistical significance on the radiologists’ performance 41,42 .”
- Why CAD Failed in Mammography. (2018, background): “Subsequent studies found that CAD neither encouraged radiologists to change their decisions nor improved their accuracy [11].”
- Deep Learning to Improve Breast Cancer Detection on Screening Mammography (2017, unknown): “embled from heterogeneous mammography platforms. Our findings show that deep learning algorithms can outperform current commercial CAD systems which have been reported to attain an average AUC of 0.72 [6]. Our all convolutional networks trained using an end-to-end approach also have highly competitiv”
- Diagnostic Accuracy of Digital Screening Mammography with and without Computer-aided Detection (2015, methodology): “Data Source Data were pooled from 5 mammography registries that participate in the Breast Cancer Surveillance Consortium (BCSC)20 funded by the National Cancer Institute: (1) the San Francisco Mammography Registry, (2) the New Mexico Mammography Advocacy Project, (3) the Vermont Breast Cancer Survei”